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Predicting Aflatoxin Contamination in Peanuts: A Genetic Algorithm/Neural Network Approach

Predicting Aflatoxin Contamination in Peanuts: A Genetic Algorithm/Neural Network Approach Aflatoxin contamination in peanut crops is a problem of significant health and financial importance. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the impact of a contaminated crop and is the goal of our research. Backpropagation neural networks have been used to model problems of this type, however development of networks poses the complex problem of setting values for architectural features and backpropagation parameters. Genetic algorithms have been used in other studies to determine parameters for backpropagation neural networks. This paper describes the development of a genetic algorithm/backpropagation neural network hybrid (GA/BPN) in which a genetic algorithm is used to find architectures and backpropagation parameter values simultaneously for a backpropagation neural network that predicts aflatoxin contamination levels in peanuts based on environmental data. Learning rate, momentum, and number of hidden nodes are the parameters that are set by the genetic algorithm. A three-layer feed-forward network with logistic activation functions is used. Inputs to the network are soil temperature, drought duration, crop age, and accumulated heat units. The project showed that the GA/BPN approach automatically finds highly fit parameter sets for backpropagation neural networks for the aflatoxin problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Predicting Aflatoxin Contamination in Peanuts: A Genetic Algorithm/Neural Network Approach

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References (24)

Publisher
Springer Journals
Copyright
Copyright © 2000 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1023/A:1008310906900
Publisher site
See Article on Publisher Site

Abstract

Aflatoxin contamination in peanut crops is a problem of significant health and financial importance. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the impact of a contaminated crop and is the goal of our research. Backpropagation neural networks have been used to model problems of this type, however development of networks poses the complex problem of setting values for architectural features and backpropagation parameters. Genetic algorithms have been used in other studies to determine parameters for backpropagation neural networks. This paper describes the development of a genetic algorithm/backpropagation neural network hybrid (GA/BPN) in which a genetic algorithm is used to find architectures and backpropagation parameter values simultaneously for a backpropagation neural network that predicts aflatoxin contamination levels in peanuts based on environmental data. Learning rate, momentum, and number of hidden nodes are the parameters that are set by the genetic algorithm. A three-layer feed-forward network with logistic activation functions is used. Inputs to the network are soil temperature, drought duration, crop age, and accumulated heat units. The project showed that the GA/BPN approach automatically finds highly fit parameter sets for backpropagation neural networks for the aflatoxin problem.

Journal

Applied Intelligence – Springer Journals

Published: Oct 1, 2004

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